tbuckley/cabot-search huggingface dataset:
Abstract LB076: A novel spatial trajectory inference method for detecting regional breast cancer progression from spatial transcriptomics data
Authors: Duy Pham, Quan Nguyen
Journal: Cancer Research (2023)

Abstract Spatial transcriptomics is a rapidly emerging field that allows for the study of gene expression within a tissue sample. This technology has the potential to provide valuable insights into the molecular mechanisms underlying cancer progression and could be particularly useful in identifying specific regional cancer progression. By studying the regional spatial trajectory of cancer progression, we can develop more targeted therapies and better predict the likelihood of metastasis. Additionally, analyzing gene expression patterns in specific regions of the tissue can reveal the molecular mechanisms driving cancer growth in those areas, potentially uncovering new therapeutic targets. In this study, we created a spatial trajectory analysis method, named as stLearn - PSeudo-Time-Space (PSTS) algorithm, to investigate specific regional cancer progression. Spatial trajectory analysis is a statistical method that allows for the identification of patterns of transitional gene expression over time and space within a tissue sample. In detail, this method incorporates spatial features, such as tissue sub-structure locations and gene expression changes, to model the trajectory from a single snapshot of transcriptomics data. Also, we evaluated the effectiveness of PSTS by utilizing the cancer progression model in human breast cancer samples. Using this approach, we were able to identify specific regions within the breast tissue that displayed distinct progression branching trajectories patterns of transitional gene expression associated with ductal carcinoma in situ and invasive ductal carcinoma. These regions were found to be characterized by a high level of cellular diversity and were associated with aggressive forms of breast cancer. The results of this study suggest that spatial transcriptomics and spatial trajectory analysis could be a powerful tool for identifying specific regional cancer progression and may help to inform the biomarkers for the development of targeted therapies for breast cancer. Overall, this study highlights the potential of spatial transcriptomics and spatial trajectory analysis to provide valuable insights into the molecular mechanisms underlying cancer progression and could have significant implications for the diagnosis and treatment of cancer. Citation Format: Duy Pham, Quan Nguyen. A novel spatial trajectory inference method for detecting regional breast cancer progression from spatial transcriptomics data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 2 (Clinical Trials and Late-Breaking Research); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(8_Suppl):Abstract nr LB076.
Abstract LB073: NCI’s broad-use collections: Accelerating discovery process by improving access to individual-level genomics and other -omics data
Authors: Freddie L. Pruitt, Michael Feolo, Subhashini Jagu, Jaime Guidry Auvil
Journal: Cancer Research (2023)

Abstract Genomic data sharing increases our understanding of factors that influence health and diseases by enabling additional research questions from secondary data users, increasing statistical power through combining multiple data sources, facilitating reproducibility and validation of research results, and supporting innovation with the development of research tools and methodologies. Research using large-scale genomic and other-omics data is no longer limited by storage space or slow downloading speed for individual datasets. Innovations like cloud infrastructure enable computing over many datasets at multiple locations at once. These developments have increased the need for faster, more efficient processes for data access and sharing. To support scientific exploration and meet the demand for analysis of biomedical data, the National Cancer Institute (NCI) has created two large collections of the broad-use studies within the database of Genotypes and Phenotypes (dbGaP). These collections comply with the consent of the study (data use limitations) for how secondary access to studies are determined. NCI’s Collection of Datasets for General Research Use comprises 284 studies with individual-level data sets, and permits approved users to explore broad research interest, including methods and tool development. NCI’s Collection of Datasets for Health, Medical, and Biomedical (HMB) Research Purposes is comprised of 65 studies of individual-level that are permitted for research interests specific to any health, medical, or biomedical research only. The HMB collection could be used for methods and tool development; research interests involving ancestry/populations studies must be dependent on a health/medical condition. Through these collections, investigators will have the potential to add access to 349 datasets to their approved research projects or submit a new project request for access to the broad-use collections. As new genomic studies are registered by NCI for release through dbGaP, they will be automatically added to these collections. NCI anticipates through the implementation of these broad-use collections a requestor could have access to approximately 70% of NCI’s studies in controlled-access repositories.The streamlined access to broad-use datasets expedites data sharing and potentially accelerates the discovery process. This approach reduces redundancies for obtaining controlled-access data. Citation Format: Freddie L. Pruitt, Michael Feolo, Subhashini Jagu, Jaime Guidry Auvil. NCI’s broad-use collections: Accelerating discovery process by improving access to individual-level genomics and other -omics data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 2 (Clinical Trials and Late-Breaking Research); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(8_Suppl):Abstract nr LB073.
Abstract LB174: Diabetes-associated breast cancer is molecularly distinct and shows DNA repair deficiency
Authors: Gatikrushna Panigrahi, Julián Candia, Tiffany H. Dorsey, Wei Tang, Yuuki Ohara, Jung S. Byun, Tsion Zewdu Minas, Amy Zhang, Anuoluwapo Ajao, Ashley Cellini, Harris G. Yfantis, Dean Mann, Olga Ioffe, Xin W. Wang, Huaitian Liu, Christopher A. Loffredo, Ana Maria Napoles, Stefan Ambs
Journal: Cancer Research (2023)

Abstract Diabetes commonly affects cancer patients. In breast cancer, it decreases patient survival. Diabetes also impacts African American women more so than European American women, potentially contributing to the breast cancer health disparity. In this study, we investigated the influence of diabetes on breast cancer biology in a diverse patient cohort using a three-pronged approach that included analysis of orthotopic human tumor xenografts, patient tumors, and breast cancer cells exposed to diabetes/hyperglycemia-like conditions. We aimed to identify shared phenotypes and molecular signatures by investigating the metabolome, transcriptome, and tumor mutational burden. Diabetes does not enhance cell proliferation but induces mesenchymal and stem cell-like phenotypes linked to increased mobility and odds of metastasis. It also promotes oxyradical formation and both transcriptome and mutational signatures of DNA repair deficiency. Moreover, food- and microbiome-derived metabolites tend to accumulate in breast tumors in presence of diabetes, potentially affecting tumor biology. Breast cancer cells cultured under hyperglycemia-like conditions acquire increased DNA damage and sensitivity to DNA repair inhibitors. Based on these observations, diabetes-associated breast tumors may show an increased drug response to DNA damage repair inhibitors that are cancer therapeutics. Key words: Breast cancer, diabetes, metabolome, transcriptome, metastasis, DNA repair, mutational signature, health disparity Citation Format: Gatikrushna Panigrahi, Julián Candia, Tiffany H. Dorsey, Wei Tang, Yuuki Ohara, Jung S. Byun, Tsion Zewdu Minas, Amy Zhang, Anuoluwapo Ajao, Ashley Cellini, Harris G. Yfantis, Dean Mann, Olga Ioffe, Xin W. Wang, Huaitian Liu, Christopher A. Loffredo, Ana Maria Napoles, Stefan Ambs. Diabetes-associated breast cancer is molecularly distinct and shows DNA repair deficiency [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 2 (Clinical Trials and Late-Breaking Research); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(8_Suppl):Abstract nr LB174.
Abstract LB168: Efficacy analysis of the DeepNeo neoantigen prediction tool for antitumor vaccine development
Authors: Eun Ji Lee, Min Ji Park, Soo Jin Kim, Seung-Jae Noh, Inkyung Shin, Jeong Yeon Kim, Incheol Shin, Jung Kyoon Choi, Dae-Yeon Cho, Suhwan Chang
Journal: Cancer Research (2023)

Abstract Antitumor peptide vaccine is an alternative and attractive way to eradicate tumors by the aid of host immune system. The current peptide vaccine development is hampered by several hurdles. These include heterogeneity of tumor cells, applicable in vivo stability of peptides, and others. One of the major obstacles is the precise prediction of cancer antigen, which can be loaded efficiently onto antigen presenting cells. We present here a neoantigen prediction algorithm DeepNeo comprised of DeepNeo-MHC and DeepNeo-TCR, which calculates the antigenicity of neoantigen based on the MHC binding and TCR activation potential. We examined the efficacy of DeepNeo using ELISPOT and two allogenic mouse tumor models. By treating the combination of long peptides targeting MC38 colon or B16F10 melanoma cell lines, we have shown the DeepNeo effectively determines neoantigens for the development of antitumor peptide vaccine. Analysis of tumor-infiltrated lymphocytes (TIL) supported our results. Undergoing study using patient-derived xenograft model treated with autologous, trained PBMC with personalized peptide vaccine will be discussed further. Citation Format: Eun Ji Lee, Min Ji Park, Soo Jin Kim, Seung-Jae Noh, Inkyung Shin, Jeong Yeon Kim, Incheol Shin, Jung Kyoon Choi, Dae-Yeon Cho, Suhwan Chang. Efficacy analysis of the DeepNeo neoantigen prediction tool for antitumor vaccine development [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 2 (Clinical Trials and Late-Breaking Research); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(8_Suppl):Abstract nr LB168.
Abstract LB171: WSI based prediction of <i>TP53</i> mutations identifies aggressive disease phenotype in prostate cancer
Authors: Marija Pizurica, Maarten Larmuseau, Kim Van der Eecken, Louise de Schaetzen van Brienen, Francisco Carrillo-Perez, Simon Isphording, Nicolaas Lumen, Jo Van Dorpe, Piet Ost, Sofie Verbeke, Olivier Gevaert, Kathleen Marchal
Journal: Cancer Research (2023)

Abstract In prostate cancer, there is an urgent need for objective prognostic biomarkers that identify a tumor’s metastatic potential at an early stage. While recent analyses indicated TP53 mutations as candidate biomarker, molecular profiling in a clinical setting is complicated by tumor heterogeneity. Deep learning models that predict the spatial presence of TP53 mutations in Whole Slide Images (WSIs) offer the potential to mitigate this issue. To assess the potential of WSIs as proxy for spatially resolved profiling or as biomarker for aggressive disease, we developed TiDo, a deep learning model that achieves state-of-the-art performance in predicting TP53 mutations from WSIs of primary prostate tumors. On an independent multi-focal cohort, we could show successful generalization of the model, both at patient and lesion level. Hence, the model offers insight into which lesions on a WSI most likely contain a TP53 mutation. Analysis of model predictions revealed that false positive (FP) predictions could at least partially be explained by TP53 deletions. This suggests that some FP carry another alteration of which the effect converges in the same histological phenotype. Comparative expression analysis and histological cell type analysis identified such common phenotype (related to stromal composition) in both TP and FP predictions. This indicates that WSI-based models might not be able to perfectly predict the spatial presence of individual TP53 mutations. However, we show they have the potential of capturing a tumor’s aggressive potential by observing a downstream phenotype of the tumor cells and TME associated with a biomarker of aggressive disease (TP53). Citation Format: Marija Pizurica, Maarten Larmuseau, Kim Van der Eecken, Louise de Schaetzen van Brienen, Francisco Carrillo-Perez, Simon Isphording, Nicolaas Lumen, Jo Van Dorpe, Piet Ost, Sofie Verbeke, Olivier Gevaert, Kathleen Marchal. WSI based prediction of TP53 mutations identifies aggressive disease phenotype in prostate cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 2 (Clinical Trials and Late-Breaking Research); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(8_Suppl):Abstract nr LB171.
Abstract LB335: Establishment of the effective dose of the STAT3 inhibitor WP1066 used in combination with STING activation for reprograming the preclinical glioma microenvironment
Authors: Hinda Najem, Shashwat Tripathi, Moloud Sooreshjani, Lisa Hurley, Corey Dussold, Victor Arrieta, Martina Ott, Jun Wei, Michael Curran, Anantha Marisetty, Waldemar Priebe, Leondias C. Platanias, Maciej S. Lesniak, Charles D. James, Craig Horbinski, David M. Ashley, Amy B. Heimberger
Journal: Cancer Research (2023)

Abstract Purpose: Trials of immunotherapy in diffuse glioma patients have been mostly unsuccessful. We therefore sought to determine the combined effects of the signal transducer and activator of transcription 3 (STAT3) inhibitor WP1066 and the STING agonist IACS-8803 in a preclinical model of glioma. Experimental Design: C57BL/6 mice (n=9-10/group) with orthotopically engrafted GL261 cells were treated orally with WP1066, a blood-brain-barrier penetrant inhibitor of STAT3, in combination with the STING agonist IACS-8803 administered directly into the tumor. Analysis of treatment effects included immunoblots, ubiquitination, multiplex immunohistochemistry, and NanoString immune phenotyping of glioma-infiltrating immune cells. Results: The STING agonist 8803 at 2.5µg/mouse, in combination with WP1066 dosed at 30mg/kg with a 12-hour delay, increased animal subject median survival (MS) to 58 days, in contrast to monotherapy (WP1066=25 days, 8803=29 days) or control (25 days) (p=0.002 of combo relative to all other groups). Dose escalation of WP1066 to 60 mg/kg, administered simultaneously with 8803, completely abrogated the therapeutic combinatorial effect. Five ubiquitin-binding domains were predicted on STING using the UbPred program; concentrations of 5µM or higher of WP1066 induced polyubiquitination of phosphorylated STING. Immune profiling during the therapeutic window demonstrated a multi-pathway induction of anti-tumor immunity. Conclusions: Co-administration of a reduced effective dose of WP1066 is necessary with STING agonist 8803 to effectively enhance the anti-glioma immune reactivity in the tumor microenvironment. Citation Format: Hinda Najem, Shashwat Tripathi, Moloud Sooreshjani, Lisa Hurley, Corey Dussold, Victor Arrieta, Martina Ott, Jun Wei, Michael Curran, Anantha Marisetty, Waldemar Priebe, Leondias C. Platanias, Maciej S. Lesniak, Charles D. James, Craig Horbinski, David M. Ashley, Amy B. Heimberger. Establishment of the effective dose of the STAT3 inhibitor WP1066 used in combination with STING activation for reprograming the preclinical glioma microenvironment [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 2 (Clinical Trials and Late-Breaking Research); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(8_Suppl):Abstract nr LB335.
Abstract LB333: Cytokine affinity tuning using the <i>AlphaSeq</i> platform to generate targeted immuno-oncology therapeutics
Authors: Ryan Swanson, Davis Goodnight, Jeff Adamo, Kyle Minch, Emily Engelhart, Randolph Lopez, David Younger
Journal: Cancer Research (2023)

Abstract Here, we demonstrate a novel approach for generating affinity detuned cytokines that can be fused to antibodies for selective immune cell activation to improve anti-tumor responses and mitigate systemic toxicity. Although cytokine therapies have demonstrated curative effects in some cancer patients, clinical use remains limited with current toxicity profiles accompanying systemic administration. Next generation cytokine approaches include conditional signaling focused to sites of interest, including the tumor microenvironment or specific immune cell populations. In this presentation, we share a novel approach for generating detuned cytokine therapeutic candidates using the AlphaSeq platform, which involves the repurposing of yeast agglutination and mating to quantitatively measure protein-protein interactions at a library-on-library scale. We show, for the first time, how AlphaSeq can be used to measure interactions between cytokines and their receptors as well as generate engineered cytokines with a broad range of affinities. A saturated mutational library was created for a cytokine of interest and subsequently screened against a second library consisting of axis receptor chains, their species orthologs and off-target receptors. Cytokine variants with lower affinity than parental were recombinantly expressed as Fc fusion proteins to orthogonally measure affinity with biolayer interferometry and characterize potency with an in vitro human PBMC phosflow assay. Finally, detuned cytokine candidates were fused to antibodies recognizing surface antigens of interest to demonstrate cell population-specific signaling. Our results show the AlphaSeq platform can accurately quantitate thousands of cytokine variant affinities simultaneously against multiple relevant receptors which translates to signaling potency in primary human cells. When engineered as antibody fusion proteins, candidate immunocytokines are identified that only induce signaling in intended cell populations. AlphaSeq’s rapid, comprehensive affinity determination can be utilized to develop a portfolio of clinically relevant therapeutic immunocytokines. Citation Format: Ryan Swanson, Davis Goodnight, Jeff Adamo, Kyle Minch, Emily Engelhart, Randolph Lopez, David Younger. Cytokine affinity tuning using the AlphaSeq platform to generate targeted immuno-oncology therapeutics [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 2 (Clinical Trials and Late-Breaking Research); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(8_Suppl):Abstract nr LB333.
Abstract LB290: Intracranial, intratumoral drug-releasing microdevices in patients with high grade gliomas may identify biomarkers of drug activity and predict tumor response to systemic chemotherapy
Authors: Oliver Jonas, Pier Paolo Peruzzi
Journal: Cancer Research (2023)

Abstract The lack of reliable predictive biomarkers to guide effective therapy is a major obstacle for the advancement of therapy for high grade gliomas (HGG), and particularly glioblastoma (GBM), one of the few cancers whose prognosis has not improved over the past several decades. With this pilot clinical trial we provide first in human evidence that drug-releasing intratumoral microdevices (IMD) can be safely and effectively used to obtain patient-specific, high throughput molecular and histopathological data to inform selection of drugs based on their observed antitumor effect in situ. The use of IMD is seamlessly integrated in standard surgical practice during tumor resection. None of the six enrolled patients experienced adverse events related to the IMD, and the retrieved tissue was usable for downstream analysis for 11 out of 12 retrieved specimens. Molecular analysis of the specimens provided, for the first time in humans, preliminary evidence of the robustness of the readout, with strong correlation between IMD analysis and clinic-radiological responses to temozolomide. We also identified novel transcriptomic and metabolomic biomarkers of response and resistance to a range of targeted and cytotoxic agents used on the IMD. From an investigational aspect, the amount of information obtained with IMD allows unprecedented characterization of tissue effects of any drugs of interest, within the physiological context of the intact tumor. Citation Format: Oliver Jonas, Pier Paolo Peruzzi. Intracranial, intratumoral drug-releasing microdevices in patients with high grade gliomas may identify biomarkers of drug activity and predict tumor response to systemic chemotherapy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 2 (Clinical Trials and Late-Breaking Research); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(8_Suppl):Abstract nr LB290.
Abstract LB065: Interface-guided phenotyping of coding variants
Authors: Kivilcim Ozturk, Rebecca Panwala, Jeanna Sheen, Prashant Mali, Hannah Carter
Journal: Cancer Research (2023)

Abstract Understanding the consequences of single amino acid substitutions in cancer driver genes remains an unmet need. High-throughput mutagenesis is emerging as a powerful tool to probe the varying consequences of different amino acid substitutions across the length of a protein or protein domain, however it is currently limited to specific functional readouts such as target protein abundance or functional assays. Studying the effects of genetic perturbations on cellular programs and fitness has been challenging using traditional pooled screens. Over the last few years, there has been a surge of interest in Perturb-seq style assays to measure the transcriptional consequences of genetic perturbations ranging from whole gene knockout to amino acid substitutions in single cells. While providing greater function insight, these sequencing-based methods are not yet scalable to exhaustive mutagenesis, necessitating selection of target mutations. In this work, we hypothesized that examining the consequences of perturbing distinct protein interactions could provide a useful abstraction of the phenotypic space reachable by individual amino acid substitutions. To explore this hypothesis, we employed a Perturb-seq style approach to generate mutations at physical interfaces of the transcription factor RUNX1, with the potential to perturb different interactions, and therefore produce transcriptional readouts implicating different aspects of the RUNX1 regulon. We analyzed these readouts to identify functionally distinct groups of RUNX1 mutations, characterize their effects on cellular programs and study the implications for cancer mutations. Our work demonstrates the potential of targeting protein interaction interfaces to better define the landscape of prospective phenotypes reachable by amino acid substitutions. Citation Format: Kivilcim Ozturk, Rebecca Panwala, Jeanna Sheen, Prashant Mali, Hannah Carter. Interface-guided phenotyping of coding variants [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 2 (Clinical Trials and Late-Breaking Research); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(8_Suppl):Abstract nr LB065.